An example of a conventional cell recognition processing is disclosed in Patent Document 1. According to the cell recognition process, a cell shape is superimposed on an image obtained by imaging a cell, and a position of a cell center is thereby extracted; pixel candidates defining a borderline of the cell are selected based upon a degree of coincidence of a direction of a luminance gradient of each pixel around the cell center and a direction toward the cell center from the each pixel; and a dynamic borderline is extracted using information on a magnitude of the luminance gradient corresponding to a position of the selected pixel candidates, thereby acquiring the borderline of the cell in detail.
The cell recognition process will be described in detail, focusing on its features. First, a method using a circular shape as a model for the cell shape is used to detect the cell center. Specifically, every cell within the image is subject to the following process performed such that, the luminance gradient direction of each target pixel within the image is calculated; and a pixel position which is a predetermined radius away in the gradient direction from the target pixel is given a point. As the result of such a process, the position where the luminance gradients which are a predetermined radius away from the periphery concentrate scores high points and is regarded as the cell center. Next, the borderline pixel candidate is selected based on the fact that the luminance gradient of the cell borderline is directed toward the cell center. The borderline pixel candidate is selected based upon a mathematical sign corresponding to an inner product of a luminance gradient vector of the target pixel located around the cell center and a displacement vector directed toward the cell center from the target pixel. In a dynamic borderline extraction process, a circular closed curved line of a specified radius around the cell center is set. If the closed curved line is on the borderline pixel candidate, the magnitude of the luminance gradient for the position of this pixel is subtracted. Such subtraction is performed for the entire closed curved line in order to attain an evaluation value. This evaluation value is minimized where the closed curved line coincides with the borderline candidate pixel to the greatest degree. Accordingly, while the shape of the closed curved line is partially changed, other evaluation values are repeatedly obtained in the same manner. The closed curved line when the obtained evaluation value is the smallest is regarded as a final cell borderline.
Examples of an area dividing method used for extracting a target area within an image include the Watershed algorithm (see Nonpatent Literature 1). The Watershed algorithm is a method for dividing an image, such as to divide an image by a boundary that is formed between different hollows where water stays when filling water into a topography regarding image grayscale information (e.g., luminance) as altitude.
FIG. 33A to FIG. 33D are diagrams explaining a principle of a process of the Watershed algorithm. FIGS. 33A to 33D show a one-dimensional example for simplified explanation. Firstly, a pixel having a minimum value is detected from luminance data of an image, as shown in FIG. 33A. Here, the pixel having the minimum value refers to a pixel having a luminance of which is the lowest among adjacent pixels. Next, as shown in FIG. 33B, areas are expanded from the minimum value in a similar manner as water stays in the hollows of luminance. In the expansion process, a boundary (Watershed) is formed at a position where areas expanded from different minimum values meet with each other, as shown in FIG. 33C. Finally, as shown in FIG. 33D, the image is divided by the boundary formed when the quantity of water held is greater than the maximum luminance. In the example shown in FIG. 33D, the image is divided into areas A, B, C and D by the forgoing process.
Meanwhile, there are two concepts of adjacent pixels in a two-dimensional grid image: one is a four adjacent pattern (refer to FIG. 34A) in which the target pixel in the middle has four adjacent pixels in the vertical and horizontal directions; and an eight adjacent pattern (refer to FIG. 34B) in which the target pixel in the middle has eight adjacent pixels in the vertical and horizontal directions and in the diagonal directions. The processes of detecting minimum values and expanding areas are carried out in accordance with either one of the concepts.    Patent Document 1: Japanese Patent No. 3314759.    Nonpatent Literature 1: Luc Vincent and Pierre Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 583-598, June 1991.